Abstract: Sign language, a language used by Deaf community, is a fully visual language with its own grammar. The Deaf people find it very difficult to express their feelings to the other people, since the other people lack the knowledge of the sign language used by the Deaf community. Due to the differences in vocabulary and grammar of the sign languages, complete adoption of methods used for other international sign languages is not possible for Indian Sign Language (ISL) recognition. It is difficult to handle continuous sign language sentence recognition and translation into text as no large video dataset for ISL sentences is available. INSIGNVID - the first Indian Sign Language video dataset has been proposed and with this dataset as input, a novel approach is presented that converts video of ISL sentence in appropriate English sentence using transfer learning. The proposed approach gives promising results on our dataset with MobilNetV2 as pretrained model.

Kinjal Mistree, Devendra Thakor and Brijesh Bhatt, “Towards Indian Sign Language Sentence Recognition using INSIGNVID: Indian Sign Language Video Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120881

@article{Mistree2021,
title = {Towards Indian Sign Language Sentence Recognition using INSIGNVID: Indian Sign Language Video Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120881},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120881},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Kinjal Mistree and Devendra Thakor and Brijesh Bhatt}
}